Towards Automatic Identification of JavaScript-oriented Machine-Based Tracking
| Autor: | Minaxi Gupta, Andrew J. Kaizer |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
World Wide Web
Information retrieval Computer science BitTorrent tracker 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology JavaScript computer Classifier (UML) computer.programming_language |
| Zdroj: | IWSPA@CODASPY |
| Popis: | Machine-based tracking is a type of behavior that extracts information on a user's machine, which can then be used for fingerprinting, tracking, or profiling purposes. In this paper, we focus on JavaScript-oriented machine-based tracking as JavaScript is widely accessible in all browsers. We find that coarse features related to JavaScript access, cookie access, and URL length subdomain information can perform well in creating a classifier that can identify these machine-based trackers with 97.7% accuracy. We then use the classifier on real-world datasets based on 30-minute website crawls of different types of websites -- including websites that target children and websites that target a popular audience -- and find 85%+ of all websites utilize machine-based tracking, even when they target a regulated group (children) as their primary audience. |
| Databáze: | OpenAIRE |
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